GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems

Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to...

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Published in:Proceedings / International Conference on Software Engineering pp. 411 - 423
Main Authors: Liang, Linfeng, Deng, Yao, Morton, Kye, Kallinen, Valtteri, James, Alice, Seth, Avishkar, Kuantama, Endrowednes, Mukhopadhyay, Subhas, Han, Richard, Zheng, Xi
Format: Conference Proceeding
Language:English
Published: IEEE 26.04.2025
Subjects:
Autonomous aerial vehicles
Autonomous systems
Diversity reception
Genetic Algorithm
Genetic algorithms
Reinforcement learning
Safety
Search-based testing
Testing
Training
Trajectory
UAV auto-landing system
Vehicle dynamics
ISSN:1558-1225
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Abstract Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to significant losses, necessitating rigorous simulation-based testing for safety. Traditional offline testing methods, limited to static environments and predefined trajectories, may miss violation cases caused by dynamic objects like people and animals. Conversely, online testing methods require extensive training time, which is impractical with limited budgets. To address these issues, we introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL) for efficient generation of diverse and real landing system failures within a practical budget. GARL employs GA for exploring various environment setups offline, reducing the complexity of RL's online testing in simulating challenging landing scenarios. Our approach outperforms existing methods by up to 18.35% in violation rate and 58% in diversity metric. We validate most discovered violation types with real-world UAV tests, pioneering the integration of offline and online testing strategies for autonomous systems. This method opens new research directions for online testing, with our code and supplementary material available at https://github.com/lfeng0722/drone_testig/.
AbstractList Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to significant losses, necessitating rigorous simulation-based testing for safety. Traditional offline testing methods, limited to static environments and predefined trajectories, may miss violation cases caused by dynamic objects like people and animals. Conversely, online testing methods require extensive training time, which is impractical with limited budgets. To address these issues, we introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL) for efficient generation of diverse and real landing system failures within a practical budget. GARL employs GA for exploring various environment setups offline, reducing the complexity of RL's online testing in simulating challenging landing scenarios. Our approach outperforms existing methods by up to 18.35% in violation rate and 58% in diversity metric. We validate most discovered violation types with real-world UAV tests, pioneering the integration of offline and online testing strategies for autonomous systems. This method opens new research directions for online testing, with our code and supplementary material available at https://github.com/lfeng0722/drone_testig/.
Author Seth, Avishkar
Kallinen, Valtteri
Zheng, Xi
Morton, Kye
Deng, Yao
James, Alice
Mukhopadhyay, Subhas
Liang, Linfeng
Kuantama, Endrowednes
Han, Richard
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  fullname: Zheng, Xi
  email: james.zheng@mq.edu.au
  organization: School of Computing, Macquarie University,Australia
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Snippet Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves...
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StartPage 411
SubjectTerms Autonomous aerial vehicles
Autonomous systems
Diversity reception
Genetic Algorithm
Genetic algorithms
Reinforcement learning
Safety
Search-based testing
Testing
Training
Trajectory
UAV auto-landing system
Vehicle dynamics
Title GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems
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